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Extracting and Visualizing Stock Data

Description

Extracting essential data from a dataset and displaying it is a necessary part of data science; therefore individuals can make correct decisions based on the data. In this assignment, you will extract some stock data, you will then display this data in a graph.

Table of Contents

  • Define a Function that Makes a Graph
  • Question 1: Use yfinance to Extract Stock Data
  • Question 2: Use Webscraping to Extract Tesla Revenue Data
  • Question 3: Use yfinance to Extract Stock Data
  • Question 4: Use Webscraping to Extract GME Revenue Data
  • Question 5: Plot Tesla Stock Graph
  • Question 6: Plot GameStop Stock Graph

Estimated Time Needed: 30 min

Note:- If you are working Locally using anaconda, please uncomment the following code and execute it. Use the version as per your python version.

In [1]:
!pip install yfinance
!pip install bs4
!pip install nbformat
!pip install matplotlib

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In [2]:
!pip install yfinance
!pip install plotly
!pip install requests
!pip imstall beautifulsoup4 
!pip install html5lib

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In [4]:
import yfinance as yf
import pandas as pd
import requests
from bs4 import BeautifulSoup

In Python, you can ignore warnings using the warnings module. You can use the filterwarnings function to filter or ignore specific warning messages or categories.

In [5]:
import warnings
# Ignore all warnings
warnings.filterwarnings("ignore", category=FutureWarning)

Define Graphing Function¶

In this section, we define the function make_graph. You don't have to know how the function works, you should only care about the inputs. It takes a dataframe with stock data (dataframe must contain Date and Close columns), a dataframe with revenue data (dataframe must contain Date and Revenue columns), and the name of the stock.

In [6]:
# The make_graph function has been modified to use Matplotlib for static graphs. Earlier, it used Plotly to generate interactive dashboards, which caused issues when uploading the notebook in the MARK assignment submission.

import matplotlib.pyplot as plt

def make_graph(stock_data, revenue_data, stock):
    stock_data_specific = stock_data[stock_data.Date <= '2021-06-14']
    revenue_data_specific = revenue_data[revenue_data.Date <= '2021-04-30']

    fig, axes = plt.subplots(2, 1, figsize=(12, 8), sharex=True)

    # Stock price
    axes[0].plot(pd.to_datetime(stock_data_specific.Date), stock_data_specific.Close.astype("float"), label="Share Price", color="blue")
    axes[0].set_ylabel("Price ($US)")
    axes[0].set_title(f"{stock} - Historical Share Price")

    # Revenue
    axes[1].plot(pd.to_datetime(revenue_data_specific.Date), revenue_data_specific.Revenue.astype("float"), label="Revenue", color="green")
    axes[1].set_ylabel("Revenue ($US Millions)")
    axes[1].set_xlabel("Date")
    axes[1].set_title(f"{stock} - Historical Revenue")

    plt.tight_layout()
    plt.show()
In [7]:
!pip install matplotlib

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In [8]:
import matplotlib.pyplot as plt

Use the make_graph function that we’ve already defined. You’ll need to invoke it in questions 5 and 6 to display the graphs and create the dashboard.

Note: You don’t need to redefine the function for plotting graphs anywhere else in this notebook; just use the existing function.

Question 1: Use yfinance to Extract Stock Data¶

Using the Ticker function enter the ticker symbol of the stock we want to extract data on to create a ticker object. The stock is Tesla and its ticker symbol is TSLA.

In [14]:
import yfinance as yf 

# Create a Ticker object for Tesla
tesla = yf.Ticker("TSLA")

Using the ticker object and the function history extract stock information and save it in a dataframe named tesla_data. Set the period parameter to "max" so we get information for the maximum amount of time.

In [10]:
import yfinance as yf 
import pandas as pd 

# Create Ticker object for Tesla
tesla = yf.Ticker("TSLA")

# Extract historical stock data for maximum period
tesla_data = tesla.history(period="max")

# Optional: reset the index to make 'Date' a column
tesla_data = tesla_data.reset_index()


# Display the first few rows
print(tesla_data.head())

                       Date      Open      High       Low     Close  \
0 2010-06-29 00:00:00-04:00  1.266667  1.666667  1.169333  1.592667   
1 2010-06-30 00:00:00-04:00  1.719333  2.028000  1.553333  1.588667   
2 2010-07-01 00:00:00-04:00  1.666667  1.728000  1.351333  1.464000   
3 2010-07-02 00:00:00-04:00  1.533333  1.540000  1.247333  1.280000   
4 2010-07-06 00:00:00-04:00  1.333333  1.333333  1.055333  1.074000   

      Volume  Dividends  Stock Splits  
0  281494500        0.0           0.0  
1  257806500        0.0           0.0  
2  123282000        0.0           0.0  
3   77097000        0.0           0.0  
4  103003500        0.0           0.0

Reset the index using the reset_index(inplace=True) function on the tesla_data DataFrame and display the first five rows of the tesla_data dataframe using the head function. Take a screenshot of the results and code from the beginning of Question 1 to the results below.

In [11]:
# Reset the index in-place so 'Date' becomes a column 
tesla_data.reset_index(inplace=True) 

# Display the first five rows
print(tesla_data.head()) 

   index                      Date      Open      High       Low     Close  \
0      0 2010-06-29 00:00:00-04:00  1.266667  1.666667  1.169333  1.592667   
1      1 2010-06-30 00:00:00-04:00  1.719333  2.028000  1.553333  1.588667   
2      2 2010-07-01 00:00:00-04:00  1.666667  1.728000  1.351333  1.464000   
3      3 2010-07-02 00:00:00-04:00  1.533333  1.540000  1.247333  1.280000   
4      4 2010-07-06 00:00:00-04:00  1.333333  1.333333  1.055333  1.074000   

      Volume  Dividends  Stock Splits  
0  281494500        0.0           0.0  
1  257806500        0.0           0.0  
2  123282000        0.0           0.0  
3   77097000        0.0           0.0  
4  103003500        0.0           0.0

Question 2: Use Webscraping to Extract Tesla Revenue Data¶

In [ ]:
In [6]:
import requests 
# URL of the webpage 

url = "https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/revenue.htm" 

# Send GET request to download the webpage 
response = requests.get(url) 

# Save the HTML text of the response 
html_data = response.text

# Optional: check the first 500 characters 
print(html_data[:500]) 

<!DOCTYPE html>
<!--[if lt IE 7]>      <html class="no-js lt-ie9 lt-ie8 lt-ie7"> <![endif]-->
<!--[if IE 7]>         <html class="no-js lt-ie9 lt-ie8"> <![endif]-->
<!--[if IE 8]>         <html class="no-js lt-ie9"> <![endif]-->
<!--[if gt IE 8]><!--> <html class="no-js"> <!--<![endif]-->
    <head>
        <meta charset="utf-8">
        <meta http-equiv="X-UA-Compatible" content="IE=edge,chrome=1">
		<link rel="canonical" href="https://www.macrotrends.net/stocks/charts/TSLA/tesla/revenue" />

Parse the html data using beautiful_soup using parser i.e html5lib or html.parser.

In [7]:
import requests
from bs4 import BeautifulSoup

url = "https://example.com"
response = requests.get(url)

html_data = response.text  # HTML stored here

soup = BeautifulSoup(html_data, "html.parser")   # or "html5lib"

print(soup.prettify()[:500])

<!DOCTYPE html>
<html lang="en">
 <head>
  <title>
   Example Domain
  </title>
  <meta content="width=device-width, initial-scale=1" name="viewport"/>
  <style>
   body{background:#eee;width:60vw;margin:15vh auto;font-family:system-ui,sans-serif}h1{font-size:1.5em}div{opacity:0.8}a:link,a:visited{color:#348}
  </style>
 </head>
 <body>
  <div>
   <h1>
    Example Domain
   </h1>
   <p>
    This domain is for use in documentation examples without needing permission. Avoid use in operations.
   <
In [9]:
import requests
url = "https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/revenue.htm"
response = requests.get(url) 
html_data = response.text
In [10]:
!pip install html5lib 

Requirement already satisfied: html5lib in /opt/conda/lib/python3.12/site-packages (1.1)
Requirement already satisfied: six>=1.9 in /opt/conda/lib/python3.12/site-packages (from html5lib) (1.17.0)
Requirement already satisfied: webencodings in /opt/conda/lib/python3.12/site-packages (from html5lib) (0.5.1)

Using BeautifulSoup or the read_html function extract the table with Tesla Revenue and store it into a dataframe named tesla_revenue. The dataframe should have columns Date and Revenue.

Step-by-step instructions 

Here are the step-by-step instructions:

1. Create an Empty DataFrame
2. Find the Relevant Table
3. Check for the Tesla Quarterly Revenue Table
4. Iterate Through Rows in the Table Body
5. Extract Data from Columns
6. Append Data to the DataFrame
In [11]:
import requests
from bs4 import BeautifulSoup
import pandas as pd


# Request webpage
html_data = requests.get(url).text

# Parse HTML 
soup = BeautifulSoup(html_data, "html.parser")

# Find table
table = soup.find("table") 

# Extract rows
rows = table.find_all("tr") 

data = [] 

for row in rows[1:]: # skip header
    cols = row.find_all("td") 
    if len(cols) > 0: 
        date = cols[0].text.strip() 
        revenue = cols[1].text 
        data.append([data, revenue]) 

# Create DataFrame 
tesla_revenue = pd.DataFrame(data, columns=["Date", "Revenue"]) 

# Clean Revenue column 
tesla_revenue["Revenue"] = tesla_revenue["Revenue"].str.replace(",", "").str.replace("$", "")

tesla_revenue.head()
Out[11]:
Date Revenue
0 [[[[[[...], [[...], '$31,536'], [[...], '$24,5... 53823
1 [[[[[[...], [[...], '$31,536'], [[...], '$24,5... 31536
2 [[[[[[...], [[...], '$31,536'], [[...], '$24,5... 24578
3 [[[[[[...], [[...], '$31,536'], [[...], '$24,5... 21461
4 [[[[[[...], [[...], '$31,536'], [[...], '$24,5... 11759
Click here if you need help locating the table 
    
Below is the code to isolate the table, you will now need to loop through the rows and columns like in the previous lab
    
soup.find_all("tbody")[1]
    
If you want to use the read_html function the table is located at index 1

We are focusing on quarterly revenue in the lab.

Execute the following line to remove the comma and dollar sign from the Revenue column.

In [22]:
tesla_revenue["Revenue"] = tesla_revenue['Revenue'].str.replace(",","").str.replace("$", "")

Execute the following lines to remove an null or empty strings in the Revenue column.

In [12]:
# Remove empty strings
tesla_revenue = tesla_revenue[tesla_revenue["Revenue"] != ""] 

# Drop null values 
tesla_revenue.dropna(inplace=True)

Display the last 5 row of the tesla_revenue dataframe using the tail function. Take a screenshot of the results.

In [13]:
import requests
from bs4 import BeautifulSoup
import pandas as pd

url = "https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/revenue.htm"
html_data = requests.get(url).text
soup = BeautifulSoup(html_data, "html.parser")

tesla_revenue = pd.DataFrame(columns=["Date", "Revenue"])

for table in soup.find_all("table"):
    if "Tesla Quarterly Revenue" in str(table):
        for row in table.find("tbody").find_all("tr"):
            col = row.find_all("td")
            if len(col) != 0:
                tesla_revenue = pd.concat(
                    [tesla_revenue,
                     pd.DataFrame({"Date":[col[0].text.strip()],
                                   "Revenue":[col[1].text.strip()]})],
                    ignore_index=True
                )

tesla_revenue["Revenue"] = tesla_revenue["Revenue"].str.replace(",", "").str.replace("$", "")

tesla_revenue.tail()
Out[13]:
Date Revenue
49 2010-06-30 28
50 2010-03-31 21
51 2009-12-31
52 2009-09-30 46
53 2009-06-30 27
In [44]:
import pandas as pd
import requests
from bs4 import BeautifulSoup
In [45]:
url = "https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/revenue.htm"
response = requests.get(url)
html_data = response.text
In [46]:
soup = BeautifulSoup(html_data, "html.parser")
In [12]:
import requests

url = "https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/revenue.html"
response = requests.get(url)
html_data = response.text

print(html_data[:500])  # print first 500 characters to see if it loaded correctly

<?xml version="1.0" encoding="UTF-8" standalone="yes"?><Error><Code>NoSuchKey</Code><Message>The specified key does not exist.</Message><Resource>/cf-courses-data/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/revenue.html</Resource><RequestId>96bfaecf-6bba-40a6-9641-e21e08731079</RequestId><httpStatusCode>404</httpStatusCode></Error>
In [42]:
tesla_revenue.tail()
Out[42]:
Date Revenue
5 2021-06-30 11958
6 2021-03-31 10389
7 2020-12-31 10744
8 2020-09-30 8771
9 2020-06-30 6036
In [11]:
!pip install lxml

Collecting lxml
  Downloading lxml-6.0.2-cp312-cp312-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl.metadata (3.6 kB)
Downloading lxml-6.0.2-cp312-cp312-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl (5.3 MB)
   ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 5.3/5.3 MB 93.2 MB/s eta 0:00:00
Installing collected packages: lxml
Successfully installed lxml-6.0.2
In [10]:
!pip install pandas

Requirement already satisfied: pandas in /opt/conda/lib/python3.12/site-packages (3.0.1)
Requirement already satisfied: numpy>=1.26.0 in /opt/conda/lib/python3.12/site-packages (from pandas) (2.4.2)
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Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.12/site-packages (from python-dateutil>=2.8.2->pandas) (1.17.0)

Question 3: Use yfinance to Extract Stock Data¶

Using the Ticker function enter the ticker symbol of the stock we want to extract data on to create a ticker object. The stock is GameStop and its ticker symbol is GME.

In [14]:
!pip install yfinance

Requirement already satisfied: yfinance in /opt/conda/lib/python3.12/site-packages (1.2.0)
Requirement already satisfied: pandas>=1.3.0 in /opt/conda/lib/python3.12/site-packages (from yfinance) (3.0.1)
Requirement already satisfied: numpy>=1.16.5 in /opt/conda/lib/python3.12/site-packages (from yfinance) (2.4.2)
Requirement already satisfied: requests>=2.31 in /opt/conda/lib/python3.12/site-packages (from yfinance) (2.32.3)
Requirement already satisfied: multitasking>=0.0.7 in /opt/conda/lib/python3.12/site-packages (from yfinance) (0.0.12)
Requirement already satisfied: platformdirs>=2.0.0 in /opt/conda/lib/python3.12/site-packages (from yfinance) (4.3.6)
Requirement already satisfied: pytz>=2022.5 in /opt/conda/lib/python3.12/site-packages (from yfinance) (2024.2)
Requirement already satisfied: frozendict>=2.3.4 in /opt/conda/lib/python3.12/site-packages (from yfinance) (2.4.6)
Requirement already satisfied: peewee>=3.16.2 in /opt/conda/lib/python3.12/site-packages (from yfinance) (3.19.0)
Requirement already satisfied: beautifulsoup4>=4.11.1 in /opt/conda/lib/python3.12/site-packages (from yfinance) (4.12.3)
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In [15]:
import yfinance as yf

# Create a ticker object for GameStop
gme = yf.Ticker("GME")
In [33]:
import yfinance as yf 
gamestop = yf.Ticker("GME")
In [16]:
import yfinance as yf

Using the ticker object and the function history extract stock information and save it in a dataframe named gme_data. Set the period parameter to "max" so we get information for the maximum amount of time.

In [17]:
import yfinance as yf

# Create a ticker object for GameStop
gme = yf.Ticker("GME")

# Extract historical stock data for the maximum period
gme_data = gme.history(period="max")

# Reset the index so 'Date' becomes a column
gme_data.reset_index(inplace=True)

# Display the first 5 rows
gme_data.head()
Out[17]:
Date Open High Low Close Volume Dividends Stock Splits
0 2002-02-13 00:00:00-05:00 1.620128 1.693350 1.603296 1.691666 76216000 0.0 0.0
1 2002-02-14 00:00:00-05:00 1.712707 1.716074 1.670626 1.683251 11021600 0.0 0.0
2 2002-02-15 00:00:00-05:00 1.683251 1.687459 1.658002 1.674834 8389600 0.0 0.0
3 2002-02-19 00:00:00-05:00 1.666418 1.666418 1.578047 1.607504 7410400 0.0 0.0
4 2002-02-20 00:00:00-05:00 1.615920 1.662210 1.603296 1.662210 6892800 0.0 0.0
In [18]:
import yfinance as yf 
import pandas as pd

Reset the index using the reset_index(inplace=True) function on the gme_data DataFrame and display the first five rows of the gme_data dataframe using the head function. Take a screenshot of the results and code from the beginning of Question 3 to the results below.

In [19]:
# Reset the index so 'Date' becomes a column
gme_data.reset_index(inplace=True)

# Display the first five rows
gme_data.head()
Out[19]:
index Date Open High Low Close Volume Dividends Stock Splits
0 0 2002-02-13 00:00:00-05:00 1.620128 1.693350 1.603296 1.691666 76216000 0.0 0.0
1 1 2002-02-14 00:00:00-05:00 1.712707 1.716074 1.670626 1.683251 11021600 0.0 0.0
2 2 2002-02-15 00:00:00-05:00 1.683251 1.687459 1.658002 1.674834 8389600 0.0 0.0
3 3 2002-02-19 00:00:00-05:00 1.666418 1.666418 1.578047 1.607504 7410400 0.0 0.0
4 4 2002-02-20 00:00:00-05:00 1.615920 1.662210 1.603296 1.662210 6892800 0.0 0.0
In [20]:
import yfinance as yf

gme = yf.Ticker("GME")
gme_data = gme.history(period="max")

Question 4: Use Webscraping to Extract GME Revenue Data¶

Use the requests library to download the webpage https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/stock.html. Save the text of the response as a variable named html_data_2

In [21]:
import requests

# URL of the webpage
url = "https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/stock.html"

# Send GET request
response = requests.get(url)

# Save the text of the response
html_data_2 = response.text

# Optional: check the first 500 characters to confirm
print(html_data_2[:500]) 

<!DOCTYPE html>
<!-- saved from url=(0105)https://web.archive.org/web/20200814131437/https://www.macrotrends.net/stocks/charts/GME/gamestop/revenue -->
<html class=" js flexbox canvas canvastext webgl no-touch geolocation postmessage websqldatabase indexeddb hashchange history draganddrop websockets rgba hsla multiplebgs backgroundsize borderimage borderradius boxshadow textshadow opacity cssanimations csscolumns cssgradients cssreflections csstransforms csstransforms3d csstransitions fontface g

Parse the html data using beautiful_soup using parser i.e html5lib or html.parser.

In [23]:
from bs4 import BeautifulSoup

soup = BeautifulSoup(html_data_2, "html.parser")

Using BeautifulSoup or the read_html function extract the table with GameStop Revenue and store it into a dataframe named gme_revenue. The dataframe should have columns Date and Revenue. Make sure the comma and dollar sign is removed from the Revenue column.

Note: Use the method similar to what you did in question 2.

Click here if you need help locating the table 
    
Below is the code to isolate the table, you will now need to loop through the rows and columns like in the previous lab
    
soup.find_all("tbody")[1]
    
If you want to use the read_html function the table is located at index 1
In [24]:
import pandas as pd
from bs4 import BeautifulSoup
import requests

url = "https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/stock.html"
html_data_2 = requests.get(url).text

soup = BeautifulSoup(html_data_2, "html.parser")  # or "html5lib"

# Find all tables
tables = soup.find_all("table")

# Loop through tables to find one with "Revenue"
for table in tables:
    if "Revenue" in table.text:
        target_table = table
        break

# Extract rows
rows = target_table.find_all("tr")

data = []
for row in rows[1:]:  # skip header
    cols = row.find_all("td")
    if len(cols) == 2:
        date = cols[0].text.strip()
        revenue = cols[1].text.strip()
        data.append([date, revenue])

# Create DataFrame
gme_revenue = pd.DataFrame(data, columns=["Date", "Revenue"])

# Clean Revenue column
gme_revenue["Revenue"] = gme_revenue["Revenue"].str.replace(r"[$,]", "", regex=True)

gme_revenue.head()
Out[24]:
Date Revenue
0 2020 6466
1 2019 8285
2 2018 8547
3 2017 7965
4 2016 9364

Remove the comma and dollar sign, an null or empty strings from the Revenue column.

In [25]:
# Remove $ and commas
gme_revenue["Revenue"] = gme_revenue["Revenue"].str.replace(r"[$,]", "", regex=True)

# Remove rows where Revenue is empty or null
gme_revenue = gme_revenue[gme_revenue["Revenue"].notna()]  # remove NaN
gme_revenue = gme_revenue[gme_revenue["Revenue"] != ""]     # remove empty strings

# Optionally convert Revenue to numeric
gme_revenue["Revenue"] = pd.to_numeric(gme_revenue["Revenue"])

# Display first 5 rows
gme_revenue.head()
Out[25]:
Date Revenue
0 2020 6466
1 2019 8285
2 2018 8547
3 2017 7965
4 2016 9364

Display the last five rows of the gme_revenue dataframe using the tail function. Take a screenshot of the results.

In [37]:
gme_revenue.tail()
Out[37]:
Date Revenue
11 2009 8806
12 2008 7094
13 2007 5319
14 2006 3092
15 2005 1843

Question 5: Plot Tesla Stock Graph¶

Use the make_graph function to graph the Tesla Stock Data, also provide a title for the graph. Note the graph will only show data upto June 2021.

Hint 

You just need to invoke the make_graph function with the required parameter to print the graphs.The structure to call the `make_graph` function is `make_graph(tesla_data, tesla_revenue, 'Tesla')`.
In [32]:
import plotly.graph_objects as go
from plotly.subplots import make_subplots

def make_graph(stock_data, revenue_data, stock):
    
    fig = make_subplots(rows=2, cols=1, shared_xaxes=True,
                        subplot_titles=("Historical Share Price", "Historical Revenue"),
                        vertical_spacing = 0.1)
    
    # Stock Data
    stock_data_specific = stock_data[stock_data['Date'] <= '2021-06-30']
    fig.add_trace(go.Scatter(x=stock_data_specific.Date, 
                             y=stock_data_specific.Close.astype("float"), 
                             name="Share Price"), 
                  row=1, col=1)

    # Revenue Data
    revenue_data_specific = revenue_data[revenue_data['Date'] <= '2021-06-30']
    fig.add_trace(go.Scatter(x=revenue_data_specific.Date, 
                             y=revenue_data_specific.Revenue.astype("float"), 
                             name="Revenue"), 
                  row=2, col=1)

    fig.update_layout(
        showlegend=False,
        height=700,
        title=stock,
        xaxis_title="Date",
        yaxis_title="Price ($US)",
        xaxis2_title="Date",
        yaxis2_title="Revenue ($US Millions)"
    )

    fig.show()
In [33]:
make_graph(tesla_data, tesla_revenue, 'Tesla')

Question 6: Plot GameStop Stock Graph¶

Use the make_graph function to graph the GameStop Stock Data, also provide a title for the graph. The structure to call the make_graph function is make_graph(gme_data, gme_revenue, 'GameStop'). Note the graph will only show data upto June 2021.

Hint 

You just need to invoke the make_graph function with the required parameter to print the graphs.The structure to call the `make_graph` function is `make_graph(gme_data, gme_revenue, 'GameStop')`
In [41]:
gme_data.reset_index(inplace=True)  # Makes the index (date) into a column
print(gme_data.columns)  # Should now include 'Date'

Index(['index', 'Date', 'Open', 'High', 'Low', 'Close', 'Volume', 'Dividends',
       'Stock Splits'],
      dtype='str')
In [42]:
make_graph(gme_data, gme_revenue, 'GameStop')

About the Authors:

Joseph Santarcangelo has a PhD in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.

Azim Hirjani

Change Log¶

Date (YYYY-MM-DD) Version Changed By Change Description
2022-02-28 1.2 Lakshmi Holla Changed the URL of GameStop
2020-11-10 1.1 Malika Singla Deleted the Optional part
2020-08-27 1.0 Malika Singla Added lab to GitLab

Copyright © 2020 IBM Corporation. All rights reserved.